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This article describes the articulation, development, and deployment of a machine learning (ML) model-driven value solution for chronic kidney disease (CKD) in a health system. The ML model activated an electronic medical record (EMR) trigger that alerted CKD patients to seek primary care. Simultaneously, primary care physicians (PCPs) received an alert that a CKD patient needed an appointment. Using structured checklists, PCPs addressed and controlled comorbid conditions, reconciled drug dosing and choice to CKD stage, and ordered prespecified laboratory and imaging tests pertinent to CKD. After completion of checklist prescribed tasks, PCPs referred patients to nephrology. CKD patients had multiple comorbidities and ML recognition of CKD provided a facile insight into comorbid burden. Operational results of this program have exceeded expectations and the program is being expanded to the entire health system. This paradigm of ML-driven, checklist-enabled care can be used agnostic of EMR platform to deliver value in CKD through structured engagement of complexity in health systems.

Purpose/objectives Chatbots are automated conversation pathways that users can access through text message or email on smartphones or other connected devices. In care management, they can be used to monitor patients' health conditions or recovery from procedures. This article describes nurse care managers' experiences using chatbots in patient care, illustrated through two patient case reviews. Considerations for planning and implementing chatbot technology in care management settings are discussed. Primary practice setting This care management service is part of an accountable care organization that serves 582,000 patients in University Hospitals of Cleveland, Ohio. Care management focuses on patients with chronic conditions, recent hospital discharges, and other needs. Care managers comprise a centralized team as well as embedded staff in select primary care practices. Findings/conclusions The two patient cases are exemplars from the care management program serving patients recently discharged from the hospital with ongoing chronic conditions that increase risk for readmission. Use of chatbots helped overcome obstacles to conventional care management outreach and resulted in improved outcomes and strong trusting relationships with the care managers. Implications for care management practice Patients who typically do not respond to other types of care manager outreach may respond to text message-based, asynchronous chatbot communication. Interpersonal relationships between care managers and patients can be strengthened by chatbot support. Chatbot technology tracks patients' progress and offers insights to patients and clinicians to facilitate earlier interventions when problems occur. Chatbots make frequent patient contact to collect and provide routine information, allowing care managers to spend more time on high-value interactions that require clinical judgment. Potential concerns about chatbots include effect on labor force, information security, health equity, and oversight of content.

Objective:The objective of the current study was to identify factors across the socio-ecological model (SEM) associated with adolescents’ sugar-sweetened beverage (SSB) intake.Design:This cross-sectional study surveyed adolescents using previously validated instruments. Analyses included descriptive statistics, ANOVA tests and stepwise nonlinear regression models (i.e., two-part models) adjusted to be cluster robust. Guided by SEM, a four-step model was used to identify factors associated with adolescent SSB intake – step 1: demographics (i.e., age, gender), step 2: intrapersonal (i.e., theory of planned behaviour (attitudes, subjective norms, perceived behavioural control, behavioural intentions), health literacy, media literacy, public health literacy), step 3: interpersonal (i.e., caregiver’s SSB behaviours, caregiver’s SSB rules) and step 4: environmental (i.e., home SSB availability) level variables.Setting:Eight middle schools across four rural southwest Virginia counties in Appalachia.Participants:Seven hundred ninety seventh grade students (55·4 % female, 44·6 % males, mean age 12 (sd0·5) years).Results:Mean SSB intake was 36·3 (sd42·5) fluid ounces or 433·4 (sd493·6) calories per day. In the final step of the regression model, seven variables significantly explained adolescent’s SSB consumption: behavioural intention (P< 0·05), affective attitude (P< 0·05), perceived behavioural control (P< 0·05), health literacy (P< 0·001), caregiver behaviours (P< 0·05), caregiver rules (P< 0·05) and home availability (P< 0·001).Conclusions:SSB intake among adolescents in rural Appalachia was nearly three times above national mean. Home environment was the strongest predictor of adolescent SSB intake, followed by caregiver rules, caregiver behaviours and health literacy. Future interventions targeting these factors may provide the greatest opportunity to improve adolescent SSB intake.

OBJECTIVE: To identify factors that influence the sugar-sweetened beverage (SSB) intake of caregivers of middle school-aged adolescents. DESIGN: Cross-sectional SETTING: Southwestern Virginia, U.S, part of Central Appalachia. PARTICIPANTS: Caregivers (n=362) of adolescents enrolled in the Kids SIPsmartER trial. Participants were mostly female (91%) and non-Hispanic White (96%), and 21% received SNAP benefits. MAIN OUTCOME MEASURES: Caregiver daily SSB intake and demographic, personal level, interpersonal level, and environmental level determinants. ANALYSIS: Descriptive statistics, one-way ANOVAs, step-wise regression. RESULTS: On average, caregivers consumed 25.7 (SD=33.2) fluid ounces of SSB per day. In the final model which included all variables, age (B=−0.41, P

Chatbots are automated conversation pathways that users can access through text message or email on smartphones or other connected devices. In care management, they can be used to monitor patients' health conditions or recovery from procedures. This article describes nurse care managers' experiences using chatbots in patient care, illustrated through two patient case reviews. Considerations for planning and implementing chatbot technology in care management settings are discussed.This care management service is part of an accountable care organization that serves 582,000 patients in University Hospitals of Cleveland, Ohio. Care management focuses on patients with chronic conditions, recent hospital discharges, and other needs. Care managers comprise a centralized team as well as embedded staff in select primary care practices.The two patient cases are exemplars from the care management program serving patients recently discharged from the hospital with ongoing chronic conditions that increase risk for readmission. Use of chatbots helped overcome obstacles to conventional care management outreach and resulted in improved outcomes and strong trusting relationships with the care managers.Patients who typically do not respond to other types of care manager outreach may respond to text message-based, asynchronous chatbot communication. Interpersonal relationships between care managers and patients can be strengthened by chatbot support. Chatbot technology tracks patients' progress and offers insights to patients and clinicians to facilitate earlier interventions when problems occur. Chatbots make frequent patient contact to collect and provide routine information, allowing care managers to spend more time on high-value interactions that require clinical judgment. Potential concerns about chatbots include effect on labor force, information security, health equity, and oversight of content.

Background Colorectal cancer (CRC) disparities vary by country and population group, but often have spatial features. This study of the United States state of Virginia assessed CRC outcomes, and identified demographic, socioeconomic and healthcare access contributors to CRC disparities. Methods County- and city-level cross-sectional data for 2011–2015 CRC incidence, mortality, and mortality-incidence ratio (MIR) were analyzed for geographically determined clusters (hotspots and cold spots) and their correlates. Spatial regression examined predictors including proportion of African American (AA) residents, rural-urban status, socioeconomic (SES) index, CRC screening rate, and densities of primary care providers (PCP) and gastroenterologists. Stationarity, which assesses spatial equality, was examined with geographically weighted regression. Results For incidence, one CRC hotspot and two cold spots were identified, including one large hotspot for MIR in southwest Virginia. In the spatial distribution of mortality, no clusters were found. Rurality and AA population were most associated with incidence. SES index, rurality, and PCP density were associated with spatial distribution of mortality. SES index and rurality were associated with MIR. Local coefficients indicated stronger associations of predictor variables in the southwestern region. Conclusions Rurality, low SES, and racial distribution were important predictors of CRC incidence, mortality, and MIR. Regions with concentrations of one or more factors of disparities face additional hurdles to improving CRC outcomes. A large cluster of high MIR in southwest Virginia region requires further investigation to improve early cancer detection and support survivorship. Spatial analysis can identify high-disparity populations and be used to inform targeted cancer control programming.

Introduction This review examined associations between the neighborhood food environment and health outcomes in populations with the highest obesity rates in the United States: people of low-socioeconomic status (SES), racial/ethnic minorities, and rural residents. Methods We searched multiple databases using preselected search terms through June 2017. Forty-three sources met criteria of peer-reviewed U.S. studies that tested food environment-health associations (e.g. obesity, diabetes) in the populations of interest. Results Evidence was sparse for multiple populations. For populations with multiple studies of adequate sample size, few found significant food environment-health associations. Modest evidence indicates that negative health outcomes were associated with (1) convenience store access for Black and Hispanic youth and (2) fast food access for Black and Hispanic adults and youth. Additionally, lower body weights were associated with supermarket and grocery store access in low-SES adults. Conclusion Food environment interventions may have health benefits for some populations, but additional research is needed.

PURPOSE: To explore system/staff- and patient-level opportunities to improve colorectal cancer (CRC) screening within an 11-clinic Federally Qualified Health Center (FQHC) in rural Appalachia with CRC screening rates around 22%-30%. METHODS: Using a convergent parallel mixed-methods design, staff (n = 26) and patients (n = 60, age 50-75, 67% female, 83%

Objective To describe how characteristics of food retail stores (potential access) and other factors influence self-reported food shopping behavior (realized food access) among low-income, rural Central Appalachian women. Design and Sample Cross-sectional descriptive. Potential access was assessed through store mapping and in-store food audits. Factors influencing consumers’ realized access were assessed through in-depth interviews. Results were merged using a convergent parallel mixed methods approach. Food stores (n = 50) and adult women (n = 9) in a rural Central Appalachian county. Results Potential and realized food access were described across five dimensions: availability, accessibility, affordability, acceptability, and accommodation. Supermarkets had better availability of healthful foods, followed by grocery stores, dollar stores, and convenience stores. On average, participants lived within 10 miles of 3.9 supermarkets or grocery stores, and traveled 7.5 miles for major food shopping. Participants generally shopped at the closest store that met their expectations for food availability, price, service, and atmosphere. Participants’ perceptions of stores diverged from each other and from in-store audit findings. Conclusions Findings from this study can help public health nurses engage with communities to make affordable, healthy foods more accessible. Recommendations are made for educating low-income consumers and partnering with food stores.